3D Imaging for Vehicle Storage Dimensioning
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Solution Overview
Problem
The transportation industry faces challenges in managing and optimizing the loading of commercial trailers due to various sizes and configurations, which complicates the tracking of loading efficiency and accuracy, especially with different types of trailers like straight-rail and drop-frame trailers, where camera-based analytics struggle with dimensioning and detecting items in complex compartments.
Innovation Solution
A 3D imaging system utilizing a 3D-depth camera and data analytics application to capture and analyze 3D image data, distinguishing between straight-rail and drop-frame trailers based on the number of planar regions detected, allowing for accurate dimensioning and tracking of vehicle storage areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera-based analytics are used to track loading efficiency, then operational metrics can be obtained, but measurement precision deteriorates for drop-frame trailers with complex compartments
Solution Approach 1:
The patent transitions from 2D camera-based analytics to 3D imaging systems, adding a depth dimension to accurately measure and differentiate trailer floor configurations. The 3D imaging capability enables precise detection of raised floors and multiple compartments in drop-frame trailers, resolving dimensioning inaccuracies that plague 2D systems.
Solution Approach 2:
The system changes the imaging parameter from standard 2D photography to 3D depth imaging, fundamentally altering how trailer interiors are captured and analyzed. This parameter change enables the system to detect vertical floor variations and compartment structures that are invisible to conventional 2D cameras.
2Loss of information
If simple loading time metrics are used, then tracking is easy, but loss of information occurs regarding actual loading efficiency
Solution Approach 1:
The system implements continuous feedback by capturing 3D images throughout the loading process, automatically analyzing planar region changes, and providing real-time information about actual loading progress. This feedback loop replaces crude time-based metrics with precise, data-driven insights into loading efficiency and trailer utilization.
Solution Approach 2:
The patent replaces manual time-tracking methods with automated 3D imaging and computer vision analysis. Instead of relying on clock-based metrics that require human intervention, the system uses optical sensing and algorithmic processing to automatically measure loading progress, eliminating information loss associated with manual tracking.
3Device complexity
If 2D camera analytics are used for all trailer types, then device complexity is low, but measurement precision deteriorates for complex trailer configurations
Solution Approach 1:
The system adopts 3D imaging instead of 2D photography, adding the depth dimension to accurately capture the vertical variations in drop-frame trailer floors. This dimensional enhancement allows the system to distinguish between raised and lowered floor sections, enabling precise dimensioning of complex trailer configurations without increasing device complexity.
Data Source
AI summary
Three-dimensional (3D) imaging systems and methods are disclosed for detecting and dimensioning a vehicle storage area. A 3D-depth camera captures 3D image data comprising one or more 3D image datasets of the vehicle storage area during corresponding one or more image capture iterations. A 3D data analytics application (app) executing on one or more processors communicatively coupled to the 3D-depth camera and for each one or more image capture iterations, updates a number of planar regions detected within the one or more 3D image datasets. The 3D data analytics app further assigns a vehicle storage area type to the vehicle storage area based on the number of planar regions detected by the 3D analytics app over the one or more image capture iterations.